This topic is particularly interesting because I think machine learning appears to be (at least in the news) a lot more prevalent in private sectors than the public sector. I guess that might have to do with the intense competition in the private sector, which leads to companies trying to gain an “edge” through advanced machine learning techniques. I do believe that the public sector should advance its technology where possible to better serve its people (consumers).
Personally, I think it makes a lot of sense to partner with private companies to collect data. One of the things that come to mind would be traffic data from ride-sharing companies such as Uber or Lyft. Because of the sheer amount and quality of traffic data that these companies possess across different time periods (peak, non-peak, etc), Boston can utilize them to improve its roads in terms of congestion and safety. While it is possible to develop its own technologies and capabilities to obtain such data, it might be a lot more costly to do so. However, you rightly pointed out the concern of data privacy. In China, this is actually done in an interesting way where a mobile app will ask you if you would like to authorize your e-commerce data (for example) from Alibaba or telecommunications data from a China Telecom. In this way, citizens will know for a fact that their data is being used if they authorize companies to transfer such data to the city. I find this to be an efficient way or obtaining data for the city of Boston without having to develop massive infrastructure (which costs money) by themselves.
This is a restaurant that I really wanted to try before it closed down but unfortunately was not able to do so. I am skeptical about a couple of points made.
Firstly, while I think it is theoretically possible for Ferran Adria to use machine learning to predict stocks of local produce in order to better manage his inventory costs, I believe he will face a lot of difficulty in obtaining the data to do so. In addition, most high-end fine dining restaurants do not actually use “mass-market” produce and typically have their own local farmer who’d deliver a specific produce. Given this context, I doubt that it is practicable for him to apply machine learning to predict quantities of local produce. In addition, I also think it would be difficult for him to obtain data on the quality of the local produce in order to apply machine learning.
I am also skeptical about the use of machine learning to process customer feedback. Given that his restaurants only cater to a relatively small number of customers every year (as compared to more mass market restaurants), I think the effects of using machine learning to process customer feedback and obtain customer insight is rather limited. In addition, I also wonder if this impedes the innovation process since he will “constrain” himself by limiting his ideas to customer preferences, which may or may not be as informed as his own.
This is an incredible use case of additive manufacturing and I believe this can truly be a game changer in terms of administering humanitarian aid. I think your question is extremely valid – how can we make additive manufacturing cost-effective in areas that are already devastated by natural disasters. My first thought was that the additive manufacturing machines need not be situated in such commonly-devastated-areas since there is a distinct possibility that such machines may be destroyed during the disasters. Instead, these machines can be located within cities which 1) are relatively well-developed so that there will be sufficient electricity to power the machines and 2) have relatively lower risk of being hit by natural disasters. In this way, even though we still have a “last-mile” delivery challenge from manufacturing city to disaster city, it is at least within the same country and will significantly shorten delivery times.
You mentioned that 3D printing of Adidas’ shoes will lead to a shorter product development cycle which will ultimately lead to faster product improvements. I am not particularly convinced at this point that the product development cycle will be significantly improved based on my elementary understanding of shoe manufacturing. I am working under the assumption that a very limited number of shoes are produced during the product development phase for testing purposes, which is why the total time spent for developing all “testing” shoes should not be that much longer without 3D printing. So, unless there is a significant set up time prior to manufacturing “testing” shoes, or that purchasing or creating of the equipment needed to manufacture such shoes require more time or effort, I am not sure if there will be an improvement in product development and improvement time/cycles.
From a customer standpoint, I do think that personalized shoes are huge. As someone who has had relatively flat feet and owns custom insoles since the age of 12, I can personally attest to the benefit of shoes that are customized and are actually “good” for your feet. As humans, we are on our feet for a significant amount of time every day and place enormous stress on them. Too often, we purchase shoes which look good but are actually terrible for our feet. I do hope that Adidas works with podiatrists to create personalized shoes through 3D printing which can actually benefit its customers (in addition to the performance or cool-factor).
Nokia’s Future X open innovation model reminds me of Xiaomi’s strategy. Xiaomi offers many different types of IoT products, most of which it does not manufacture by itself (with the exception of its smartphones). It is able to do so by investing in companies and helping them with financing, strategy and incorporating them into the overall ecosystem (since IoT does not have any value without being in a large-enough ecosystem).
Having said that, I question the long term viability of Nokia’s strategy. Firstly, it only requires that participants submit a 250 word essay and are “encouraged” to submit a prototype. From an efficiency standpoint, I believe Nokia will most likely receive a lot of random submissions that would not have any value at all. In contrast, Xiaomi’s open innovation model involves having a dedicated venture capital team looking at early stage companies and bringing them under the company’s umbrella early on. My guess is that Xiaomi’s approach is arguably more efficient and potentially more effective than Nokia’s.
In addition, given the early stage nature of the submissions (since only a 250 word essay and an optional prototype is required), I wonder how long it will actually take for these ideas to enter production. Given that IoT is not new and has been around for a couple of years, will the new products and services developed through Nokia still be relevant when they are launched?
While Lego Life and Lego Ideas seem to be great ideas on paper in terms of fostering open innovation and allowing customer insight to drive product development, I am not convinced that Lego is set up for the future of toys – which I am guessing will not be in a physical form (but perhaps Lego knows better). Looking at both Lego Ideas and Lego Life, it appears to me that these two platforms are still driven by physical Lego blocks and are focused on “iterations” of Lego blocks rather than breakthrough products.
My primary concern for Lego is whether its prior success in physical Lego blocks will hinder it from developing toys for the next generation (in whatever form they may be).
In addition, I am also curious to see how these two open innovation platforms will play out in the long run because my hypothesis is that kids in the current generation arguably have less exposure or experience with Lego than our generation did. As a result, they may not feel as nostalgic about the brand and hence do not feel the need or desire to help the company to innovate and create products which they would not want to play with anyways.